CS229: Machine Learning

Course Description   This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.


Course Information

Time and Location
Tuesday, Thursday 9:45 - 11:15 on Zoom
Friday TA Lectures
Friday 1:00 - 2:30 on Zoom
Office Hours
Office hours will be hosted on Nooks
Contact and Communication
Due to a large number of inquiries, we encourage you to first read the Logistics/FAQ page for commonly asked questions, and then create a post on Ed to contact the course staff. Please do NOT reach out to the instructors directly, otherwise your questions may get lost.
This quarter we will be using Ed as the course forum.
  • All official announcements and communication will happen over Ed.
  • Feel free to reach out to us on Ed for any questions. Please do NOT reach out to the instructors directly, otherwise you question might get lost.
  • Any questions regarding course content and course organization should be posted on Ed. You are strongly encouraged to answer other students' questions when you know the answer.
  • If there are private matters specific to you (e.g special accommodations, requesting alternative arrangements etc.), please create a private post on Ed.
  • For longer discussions with TAs, please attend office hours.
  • TA office hours can be found on Canvas. For the course calendar, see the Syllabus.
  • Before the beginning of the course, please contact the course coordinator Swati Dube for logistical questions (ideally after consulting the FAQ page)
Please fill out this form here, and we will review all the audit requests and add you to the course’s Canvas page. Please note that auditors do not get access to the Ed forum (partly because we often have limited TAs to answer questions online) and thus do not get access to the assignments. Likewise, auditors do not have access to Gradescope to submit assignments.